Cost-effectiveness calculation method and device

Abstract

To enable advantages and disadvantages to be grasped in introducing a system having an information acquisition function of a knowledge system or the like.SOLUTION: A cost-effectiveness calculation device for calculating cost-effectiveness in introducing a system having an information acquisition function for acquiring information represented by a knowledge system includes a cost acquisition part for acquiring cost about introduction of the system, a first modeling part for calculating a first model showing relations of the cost, an information amount showing amount of the information and information quality showing the quality of the information, a second modeling part for calculating a second model showing relations of the information amount, the information quality and information acquisition performance showing the performance of the information acquisition function by using the first model, and a cost-effectiveness calculation part for calculating cost-effectiveness in introduction the system including the cost and the information acquisition performance being an advantage / disadvantage index in the introduction of the system by using the first model and the second model.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] This invention relates to a technology for estimating the effectiveness, particularly cost-effectiveness, of introducing various computer systems, IT systems, services, etc. (hereinafter simply referred to as systems) that have an information acquisition function for obtaining information etc. to solve problems. Such systems include knowledge systems and search systems. [Background technology]

[0002] Currently, when considering the introduction of a system, estimates are prepared and the feasibility of the introduction is determined. Patent Document 1 proposes a technology for preparing such estimates. Patent Document 1 addresses the issue of "creating an estimate proposal that is highly appealing to customers in terms of the period required for optimization and total cost." To this end, Patent Document 1 proposes an estimate management system that "stores multiple past estimates, each including a proposed plan and a comparison plan. The estimate management system accepts input of estimate conditions, including multiple estimate condition items, by a user to create a new estimate, and performs estimates by setting the values ​​of the estimate condition items for each of multiple estimates selected from the multiple past estimates as values ​​for the unentered items in the multiple estimate condition items. The estimate management system generates multiple estimate results corresponding to the multiple estimates and evaluates the multiple estimate results in terms of total cost and the period required for optimization." [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-71867 Summary of the Invention [Problem to be solved by the invention]

[0004] Systems such as knowledge systems allow stakeholders to share and use information, including knowledge, insights, and know-how. This allows for problem-solving and clue-finding, thereby supporting and realizing business and operations. Therefore, when introducing a system, its contribution to business and operations must be considered as an effect. Furthermore, the labor hours required for building and maintaining the system, such as registering information, must be considered as a cost. For example, when introducing a knowledge system, it is necessary to convert know-how and other information into knowledge, and to register and update that knowledge. Thus, the decision to introduce a system is based on cost-effectiveness. Therefore, in order to determine whether or not to introduce a system, it is necessary to accurately assess the pros and cons of introduction, such as accurate cost and cost-effectiveness. Therefore, the present invention aims to more accurately grasp the pros and cons of introducing a system with information acquisition functions, such as a knowledge system. [Means for solving the problem]

[0005] In order to solve the above problems, the present invention calculates and presents a number of pros and cons indices for introducing a system, such as cost-effectiveness including the cost and the information acquisition performance of the system.

[0006] More specifically, in this cost-effectiveness calculation method, a cost-effectiveness calculation device calculates the cost-effectiveness of introducing a system having an information acquisition function that acquires information for solving a problem, wherein a cost acquisition unit acquires the cost of introducing the system, a first modeling unit calculates a first model that shows the relationship between the cost, the information quantity indicating the amount of information, and the information quality indicating the quality of the information, a second modeling unit uses the first model to calculate a second model that shows the relationship between the information quantity and the information quality, and the information acquisition performance that indicates the performance of the information acquisition function, a cost-effectiveness calculation unit uses the first model and the second model to calculate the cost-effectiveness of introducing the system, including the cost and the information acquisition performance, which are profit-loss indicators for introducing the system, and an output unit outputs the cost-effectiveness.

[0007] The present invention also includes a cost-effectiveness calculation device that executes the cost-effectiveness calculation method, a program for causing the device to function as a computer, and a storage medium that stores the program. [Effects of the Invention]

[0008] According to the present invention, it is possible to more accurately grasp the advantages and disadvantages, such as costs and cost-effectiveness, of introducing a system such as a knowledge system. [Brief explanation of the drawings]

[0009] [Figure 1] 1 is a functional block diagram of a cost-effectiveness calculation device 1 according to an embodiment of the present invention. [Figure 2] 1 is a configuration diagram showing an example of implementation of a cost-effectiveness calculation device 1 according to an embodiment of the present invention. [Figure 3] FIG. 1 is a diagram schematically illustrating knowledge 171 in one embodiment of the present invention. [Figure 4] FIG. 10 is a diagram showing a knowledge quantity definition table 172 in one embodiment of the present invention. [Figure 5] FIG. 10 is a diagram showing a knowledge quality definition table 173 in one embodiment of the present invention. [Figure 6A] FIG. 17 is a diagram showing a man-hours-knowledge quantity / quality model 174-1 for collecting information sources in one embodiment of the present invention. [Figure 6B] FIG. 17 is a diagram showing a man-hour-knowledge quantity / quality model 174-2 for narrowing down information sources in one embodiment of the present invention. [Figure 6C] FIG. 17 is a diagram showing a man-hours-knowledge quantity / quality model 174-3 for knowledge generation (knowledge creation) of an information source in one embodiment of the present invention. [Figure 6D] FIG. 17 is a diagram showing a man-hour-knowledge quantity / quality model 174-4 for registering knowledge in one embodiment of the present invention. [Figure 6E] FIG. 17 is a diagram showing a man-hour-knowledge quantity / quality model 174-5 for updating knowledge in one embodiment of the present invention. [Figure 7] FIG. 10 is a diagram showing a knowledge quantity / quality-acquisition performance model 175 in one embodiment of the present invention. [Figure 8A] FIG. 10 is a diagram showing a quantity-quality relationship definition model 176 in one embodiment of the present invention. [Figure 8B] FIG. 10 is a diagram showing a quantity-quality relationship definition model 176 in one embodiment of the present invention. [Figure 9] 1 is a flowchart showing a processing flow according to an embodiment of the present invention. [Figure 10A] FIG. 17 is a diagram showing a knowledge quantity / quality-acquisition performance model 175-1 in one embodiment of the present invention. [Figure 10B] FIG. 17 is a diagram showing a knowledge quantity / quality-acquisition performance model 175-2 in one embodiment of the present invention. [Figure 10C] FIG. 17 is a diagram showing a knowledge quantity / quality-acquisition performance model 175-3 in one embodiment of the present invention. [Figure 10D] FIG. 17 is a diagram showing a knowledge quantity / quality-acquisition performance model 175-4 in one embodiment of the present invention. [Figure 11A] FIG. 17 is a diagram showing a knowledge quantity / quality-acquisition performance model 175-5 in one embodiment of the present invention. [Figure 11B] FIG. 17 is a diagram showing a knowledge quantity / quality-acquisition performance model 175-6 in one embodiment of the present invention. [Figure 11C] FIG. 17 is a diagram showing a knowledge quantity / quality-acquisition performance model 175-7 in one embodiment of the present invention. [Figure 11D] FIG. 17 is a diagram showing a knowledge quantity / quality-acquisition performance model 175-8 in one embodiment of the present invention. [Figure 12A] FIG. 17 is a diagram showing a knowledge quantity / quality-acquisition performance model 175-9 in one embodiment of the present invention. [Figure 12B] FIG. 17 is a diagram showing a knowledge quantity / quality-acquisition performance model 175-10 in one embodiment of the present invention. [Figure 12C] FIG. 17 is a diagram showing a knowledge quantity / quality-acquisition performance model 175-11 in one embodiment of the present invention. [Figure 12D] FIG. 17 is a diagram showing a knowledge quantity / quality-acquisition performance model 175-12 in one embodiment of the present invention. [Figure 13A] FIG. 17 is a diagram showing a knowledge quantity / quality-acquisition performance model 175-13 in one embodiment of the present invention. [Figure 13B] FIG. 17 is a diagram showing a knowledge quantity / quality-acquisition performance model 175-14 in one embodiment of the present invention. [Figure 13C] FIG. 17 is a diagram showing a knowledge quantity / quality-acquisition performance model 175-15 in one embodiment of the present invention. [Figure 13D] FIG. 17 is a diagram illustrating a knowledge quantity / quality-acquisition performance model 175-16 in one embodiment of the present invention. [Figure 14] FIG. 10 is a diagram showing display screens 160-1 to 160-3 of a cost-effectiveness indicator 177 in one embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0010] In this embodiment, multiple pros and cons of introducing a system, such as cost and the system's information acquisition performance, are included, and more preferably, a cost-effectiveness indicator showing the relationship between these is calculated and presented. One aspect of this involves first modeling the relationship between cost and "information quantity and information quality," and then, based on this, performing a second modeling of the relationship between information quantity and information quality and information acquisition performance, thereby calculating the cost-effectiveness. In this way, the present invention calculates the cost-effectiveness of introducing a system through a two-stage modeling based on the information quantity and information quality of the information handled by the system. More preferably, the system is a knowledge system, and knowledge quantity and knowledge quality are used, with accuracy, granularity, and speed used as information acquisition performance. Details of this are described below.

[0011] In this embodiment, a technology for calculating the cost-effectiveness of introducing a knowledge system will be described as an example of a system with an information acquisition function. A knowledge system, also referred to as a knowledge management system, knowledge tool, or knowledge management tool, has a search function for searching for knowledge to solve problems. As a result, knowledge accumulated by individuals or departments can be shared within organizations such as companies, thereby enabling problem solving. This knowledge includes information, knowledge, know-how, and the like. Therefore, by using a knowledge system, users can understand solutions to problems and clues to those solutions. Knowledge may include solutions to problems or clues, or users may come up with solutions or clues from knowledge.

[0012] In this embodiment, knowledge is used as an example of information. In this embodiment, the man-hours required to introduce a knowledge system are used as an example of the cost of introducing a knowledge system. This cost is required to introduce the system and includes not only expenses such as investment amounts but also various burdens such as time and labor. Note that these various burdens also include burdens that arise after the system is introduced. Furthermore, the accuracy, granularity, and speed of knowledge acquisition from the knowledge system are used as information acquisition performance. These units can be used, for example, as follows: accuracy: percentage (e.g., %), granularity: quantity (e.g., pieces), speed: degree of speed or time (e.g., fast, slow). These costs and information acquisition performance can also be called gain / loss indicators, as they indicate the gains and losses, respectively, in introducing a knowledge system.

[0013] In this embodiment, the man-hours required for introducing a knowledge system are acquired, and a man-hours-knowledge quantity / quality model showing the relationship between the man-hours, knowledge quantity, and knowledge quality is calculated as a first model. Furthermore, a knowledge quantity / quality-acquisition performance model showing the relationship between the knowledge quantity, knowledge quality, and knowledge acquisition performance in the knowledge system is calculated. Based on this, in this embodiment, these models are used to calculate the cost-effectiveness of introducing a knowledge system. Details of this are explained below.

[0014] 1 is a functional block diagram of a cost-effectiveness calculation device 1 in this embodiment. The cost-effectiveness calculation device 1 calculates the cost-effectiveness when introducing a knowledge system. To this end, the cost-effectiveness calculation device 1 has an input unit 11, a labor-hour acquisition unit 12, a labor-hour-knowledge quantity / quality modeling unit 13, a knowledge quantity / quality-acquired performance modeling unit 14, a cost-effectiveness calculation unit 15, an output unit 16, and a memory unit 17.

[0015] The input unit 11 accepts input from a user or the like. This input includes an instruction to calculate cost-effectiveness. The labor-hour acquisition unit 12 acquires the labor hours for a knowledge system that is a candidate for introduction, i.e., the knowledge system for which cost-effectiveness is to be calculated. Here, the labor hours refer to the labor hours required to introduce the knowledge system (including the labor hours required after introduction), and more preferably, the labor hours required to process the knowledge. That is, in this embodiment, the labor hours are used for collecting information sources that can become data items for knowledge, narrowing down the collected information sources, converting the narrowed down information sources into knowledge, registering the created knowledge, and updating the registered knowledge. Note that it is sufficient to use at least one of these processes. The labor hours also include various elements such as costs (including labor costs) and time. Converting information into knowledge means creating knowledge by connecting the narrowed down information sources according to relationships such as causal relationships and correlations.

[0016] Here, the man-hour acquisition unit 12 may acquire the man-hours input by the user via the input unit 11, or may read out the specification data of the target knowledge system. The specification data is data indicating the functions and performance of the target knowledge system, and is stored in the storage unit 17. In this way, the man-hour acquisition unit 12 is an example of a cost acquisition unit that acquires costs.

[0017] Furthermore, the man-hour-knowledge quantity / quality modeling unit 13 calculates a man-hour-knowledge quantity / quality model by modeling the relationship between the acquired man-hours and the knowledge quantity indicating the quantity of knowledge in the target knowledge system and the knowledge quality indicating the quality of the knowledge. Therefore, the man-hour-knowledge quantity / quality modeling unit 13 is an example of a first modeling unit that calculates a first model by modeling the relationship between cost and the information quantity indicating the amount of information in the system and the information quality indicating the quality of the information.

[0018] Furthermore, the knowledge quantity / quality-acquisition performance modeling unit 14 uses the man-hour-knowledge quantity / quality model to calculate a knowledge quantity / quality-acquisition performance model that indicates the relationship between the knowledge quantity and knowledge quality and the knowledge acquisition performance of the knowledge system.

[0019] Here, knowledge acquisition performance refers to the ability of a knowledge system to acquire knowledge, and indicates, for example, the accuracy, granularity, and speed of acquisition. Accuracy refers to the accuracy of acquired knowledge, such as the accuracy rate of acquired knowledge. Therefore, accuracy also includes the problem-solving rate when using a knowledge system, such as for troubleshooting equipment failures or how to proceed with a project or case. Granularity refers to the degree to which a knowledge system narrows down problems. For example, granularity refers to the narrowing down rate of potential causes when searching for the cause of a failure as a problem. Speed ​​refers to the search speed of the search function. This search speed includes the time it takes to arrive at knowledge search results or a solution to a problem (or a clue thereto). Therefore, the knowledge quantity / quality-acquisition performance modeling unit 14 is an example of a second modeling unit that uses the first model to model the relationship between information quantity and information quality and information acquisition performance, which indicates the performance of the information acquisition function, and calculates a second model.

[0020] The cost-effectiveness calculation unit 15 calculates the cost-effectiveness of introducing a knowledge system using a knowledge quantity / quality-acquisition performance model. The cost in this cost-effectiveness refers to the cost and is not limited to monetary burden. Here, knowledge acquisition performance varies depending on the knowledge quality and quantity. For example, the speed varies depending on the knowledge quantity. Furthermore, the knowledge quantity and knowledge quality vary depending on the number of man-hours. Therefore, in this embodiment, the cost-effectiveness of introducing a knowledge system is calculated by two-stage modeling via the knowledge quantity and knowledge quality. The output unit 16 outputs the calculated cost-effectiveness. The output unit 16 may output information indicating the knowledge quantity / quality-acquisition performance model, and the cost-effectiveness calculation unit 15 may calculate the cost-effectiveness in response to user instructions on the output content from the input unit 11. In this way, the calculation and output of cost-effectiveness may be performed interactively with the user.

[0021] The storage unit 17 also stores knowledge 171, a knowledge quantity definition table 172, a knowledge quality definition table 173, a man-hour-knowledge quantity / quality model 174, a knowledge quantity / quality-acquisition performance model 175, a quantity / quality relationship definition model 176, and a cost-effectiveness 177. These will be described in detail later.

[0022] Next, an implementation example of this embodiment will be described. Below, an example will be described in which the cost-effectiveness calculation device 1 is realized by a server that executes processing according to a program. FIG. 2 is a configuration diagram showing an implementation example of the cost-effectiveness calculation device 1 in this embodiment. FIG. 2 shows an example in which the cost-effectiveness calculation device 1 is realized by a server that executes processing according to a program. For this reason, the cost-effectiveness calculation device 1 is connected to other devices such as terminal devices via a network 5.

[0023] First, in FIG. 2, the cost-effectiveness calculation device 1 has a processing device 101, a communication device 102, a main storage device 103, and a sub-storage device 104, which are connected to each other via a communication path.

[0024] Here, the processing device 101 can be realized by a processor such as a CPU, and executes calculations in accordance with various programs described below. The communication device 102 has an interface function for connecting to the network 5, and corresponds to the input unit 11 and output unit 16 in FIG. 1. The network 5 is connected to a corporate system 20 of an introducing company that is considering introducing the system, a terminal device 3, and a database system 4. These will be explained after the explanation of the configuration of the cost-effectiveness calculation device 1.

[0025] The main storage device 103 can be realized by a so-called memory, and for the purposes of calculations by the processing device 101, programs stored in storage media such as the secondary storage device 104 and information used in the processing of these programs are deployed. The secondary storage device 104 can be realized by storage such as a hard disk drive, and stores programs and various information (knowledge 171, etc.). In this way, the secondary storage device 104 corresponds to the memory unit 17 in FIG. 1. Here, the secondary storage device 104 stores a cost-effectiveness calculation program 105 as a program. The cost-effectiveness calculation program 105 is composed of a man-hour acquisition module 106, a man-hour-knowledge quantity / quality modeling module 107, a knowledge quantity / quality-acquired performance modeling module 108, and a cost-effectiveness calculation module 109.

[0026] The modules of the cost-effectiveness calculation program 105 have the following correspondence with the parts in FIG. Man-hour acquisition unit 12: Man-hour acquisition module 106 Man-hours-knowledge quantity and quality modeling part 13: Man-hours-knowledge quantity and quality modeling module 107 Knowledge quantity / quality-acquisition performance modeling unit 14: Knowledge quantity / quality-acquisition performance modeling module 108 Cost-effectiveness calculation unit 15: Cost-effectiveness calculation module 109 That is, the processing device 101 executes the processes of the man-hour acquisition unit 12, the man-hour-knowledge quantity / quality modeling unit 13, the knowledge quantity / quality-acquired performance modeling unit 14, and the cost-effectiveness calculation unit 15 in accordance with the cost-effectiveness calculation program 105.

[0027] The cost-effectiveness calculation program 105 is distributed via the network 5 or stored in a storage medium and installed in the cost-effectiveness calculation device 1. Each module may be configured as an independent program. The cost-effectiveness calculation device 1 can be realized as an estimation device or estimation support device that estimates a knowledge system. In this case, the cost-effectiveness calculation program 105 may be realized as one function of the estimation program or estimation support program.

[0028] The secondary storage device 104 also stores the following information used in this embodiment: knowledge 171, a knowledge quantity definition table 172, a knowledge quality definition table 173, a labor-hours-knowledge quantity / quality model 174, a knowledge quantity / quality-acquisition performance model 175, a quantity / quality relationship definition model 176, and a cost-effectiveness 177.

[0029] Next, other devices connected to the cost-effectiveness calculation device 1 via the network 5 will be described. First, the enterprise system 20 is a computer system of a company considering introducing a knowledge system, and has multiple terminal devices 2-1 and 2-2. These terminal devices 2-1 and 2-2 are used by company employees to receive instructions for calculating cost-effectiveness and display the cost-effectiveness calculated by the cost-effectiveness calculation device 1. For this reason, they have the functions of the input unit 11 and output unit 16 in FIG. 1. The cost-effectiveness calculation device 1 may be provided in the enterprise system 20. The terminal devices 2-1 and 2-2 can be realized by computers such as PCs and tablets.

[0030] The terminal device 3 is a management terminal that manages the cost-effectiveness calculation device 1, and can be realized by a computer such as a PC or a tablet. Therefore, the terminal device 3 may be used by the provider of the knowledge system, or by a company that manages the cost-effectiveness calculation device 1 as a cloud system. Furthermore, the terminal device 3 may be connected to the cost-effectiveness calculation device 1 via a network other than the network 5 (for example, an intranet) or directly.

[0031] Furthermore, the database system 4 stores the knowledge 171 and the cost-effectiveness 177. Therefore, the knowledge 171 and the cost-effectiveness 177 in the secondary storage device 104 may be omitted. It is desirable that the database system 4 be managed by the provider of the knowledge system. This concludes the description of an implementation example of this embodiment. The cost-effectiveness calculation device 1 may be realized by a computer such as a PC or tablet equipped with a display device and an input device. Furthermore, the cost-effectiveness calculation device 1 may be realized as a computer of a company that provides the knowledge system. Providing a knowledge system includes selling and delivering it as a corporate system, and providing its functions as a service in a cloud system, etc. Sales and delivery include not only the sale of a program, but also system construction including after-sales service. Furthermore, the cost-effectiveness calculation device 1 may be available to so-called consulting companies that provide advice to companies that adopt the system.

[0032] Next, the information used in this embodiment will be described. Fig. 3 is a diagram schematically showing knowledge 171 in this embodiment. The knowledge system in this embodiment is a system for checking countermeasures for failures in a certain device. For this reason, the knowledge 171 of the knowledge system to be analyzed in this embodiment has investigation items and failure causes as data items. Furthermore, investigation items and failure causes that have causal relationships or correlations are connected by links.

[0033] In this way, knowledge 171 is a collection of data items that are unit data, such as investigation items and failure causes that are connected according to some relationship. In the example of Figure 3, if there is an investigation item that says "function B does not work" on the device, the cause of the failure (core part H, part K, power supply M) can be identified by checking the "warning message on the panel display" and tracing the lighting and display contents of the "lamp" or "console" corresponding to the content.

[0034] 4 is a diagram showing the knowledge quantity definition table 172 in this embodiment. The knowledge quantity definition table 172 is a table for specifying the knowledge quantity of knowledge, for example, knowledge 171. For this reason, the knowledge quantity definition table 172 indicates each item of content and knowledge quantity for each perspective.

[0035] The perspective indicates the object of focus when determining the amount of knowledge, and can be a data item or the whole (knowledge). The content indicates the conditions for determining the amount of knowledge from the perspective. For example, it records whether the number of data items for problem solving is above a threshold. In the example of Figure 3, the data items for problem solving indicate investigation items and failure items, and the amount of knowledge can be determined by whether their numbers are above a threshold. In Figure 4, a simple "threshold" is shown, but it is desirable to record a specific numerical value here.

[0036] Furthermore, the knowledge amount indicates the degree of the quantity of knowledge identified in the case of the corresponding content. For example, if the quantity of a data item is equal to or greater than a threshold value, the knowledge amount is identified as "large." Note that in the example of FIG. 4, the knowledge amount is shown as two levels, "large" and "small," but the knowledge amount is not limited to this. For example, it may be three levels including "medium," or multiple levels such as 0% to 100%, or it may be infinite. Furthermore, the expressions "large" and "small" are merely examples and are not limiting. The quantity of knowledge itself may also be used as the knowledge amount.

[0037] Furthermore, in this embodiment, two perspectives, data item and the whole, are exemplified as perspectives, but the perspectives are not limited to these, and one of them may be omitted. Here, if there is a contradiction in the amount of knowledge from each perspective, such as data item and the whole, the knowledge amount from the most common perspective may be determined by majority voting, or the knowledge amount from a perspective based on a predetermined rule, such as a more severe perspective, may be determined. Note that in this embodiment, the knowledge amount is determined mechanically using the knowledge amount definition table 172, but the user may also determine the knowledge amount themselves.

[0038] Next, FIG. 5 is a diagram showing the knowledge quality definition table 173 in this embodiment. The knowledge quality definition table 173 is a table for specifying the knowledge quality of knowledge, for example, knowledge 171. Therefore, the knowledge quality definition table 173 indicates each item of content and knowledge quality for each perspective. A perspective indicates an object to be focused on when specifying knowledge quality, and may use accuracy, completeness, appropriateness, availability, and usability (accessibility). Note that accuracy, completeness, appropriateness, availability, and usability (accessibility) are merely examples, and the perspective is not limited to these, and at least some of them may be omitted. Furthermore, the content indicates the content of the conditions for specifying knowledge quality in a perspective.

[0039] Here, we will explain each perspective and its content. First, accuracy refers to the accuracy of the terms used in the knowledge. Therefore, the content of accuracy is recorded as to whether the misuse of words and phrases (terms) in the knowledge is below a threshold. Furthermore, completeness refers to the comprehensiveness of data items for analysis to solve problems using knowledge. Therefore, the content of completeness is recorded as to whether the content of the countermeasure items for solving problems is sufficient and whether the countermeasure items are linked (connected) to the causes of the problems. Here, countermeasure items are those for solving problems, and indicate, for example, the content of investigations and confirmations. Furthermore, whether they are linked can be determined by whether a certain percentage or more of them are linked. Therefore, the existence of isolated causes indicates that a certain number or percentage or more of them are isolated.

[0040] Appropriateness refers to the accuracy of the relationships between knowledge data items. Therefore, the content of appropriateness is recorded as whether the correct countermeasure items are linked to the causes of the issues. Whether the correct items are linked is determined by whether a certain number or percentage of items are correctly linked.

[0041] Additionally, availability refers to the ease with which knowledge data items can be used. Therefore, availability is recorded as a measure of whether there are no overlaps among data items. Here, "no overlaps" is determined by whether there are a certain number or percentage of overlaps. Furthermore, accessibility refers to the ease with which the analysis for problem-solving using knowledge can be understood. Therefore, accessibility is recorded as a measure of whether there are explanations and annotations for technical terms, and whether diagrams and other information are used appropriately.

[0042] Furthermore, knowledge quality indicates the level of quality of knowledge identified in the case of corresponding content. In other words, knowledge quality is identified as "high" or "low" depending on each content. Note that in the example of FIG. 5, two levels of knowledge quality, "high" and "low," are shown as examples, but knowledge quality is not limited to these. For example, knowledge quality may be three levels including "medium," or multiple levels such as 0% to 100%, or may be non-level. Furthermore, the expressions "high" and "low" are merely examples and are not limiting. Furthermore, an index of knowledge quality calculated according to a predetermined rule may be used as knowledge quality. Furthermore, in this embodiment, knowledge quality is mechanically identified using knowledge quality definition table 173, but knowledge quality may also be identified by the user himself.

[0043] Next, the labor-hours-knowledge quantity / quality model 174 in this embodiment will be described with reference to Figures 6A to 6E. The labor-hours-knowledge quantity / quality model 174 is a model that shows the relationship between the labor hours in introducing a knowledge system and the knowledge quantity and knowledge quality. In other words, it is an example of a first model that shows the relationship between the labor hours and the information quantity and information quality in a system having an information acquisition function.

[0044] In this embodiment, the man-hours (types) used are gathering information sources, narrowing down information sources, turning information sources into knowledge (creating knowledge), registering knowledge, and updating knowledge. Furthermore, for these, man-days are used as the unit of man-hours, but man-months, costs (amounts), etc. may also be used. Note that these are merely examples, and at least some may be omitted, or other man-hours may be added. Then, in this embodiment, a man-hour-knowledge quantity / quality model 174 is calculated for each man-hour. The man-hour-knowledge quantity / quality model 174 for each man-hour will be described below.

[0045] First, FIG. 6A is a diagram showing a man-hour-knowledge quantity / quality model 174-1 for collecting information sources in this embodiment. Here, the collection of information sources has a correlation with the amount of knowledge. In other words, the more man-hours spent on collecting information sources, the more the amount of knowledge improves. However, once a certain number of information sources have been collected, there will be no more information sources to collect, and the improvement in the amount of knowledge will reach an upper limit. Therefore, as shown in FIG. 6A, in the man-hour-knowledge quantity / quality model 174-1, the amount of knowledge will improve until the man-hours reach a certain level, and thereafter the amount of knowledge will be maintained.

[0046] Next, FIG. 6B is a diagram showing a man-hour-knowledge quantity / quality model 174-2 for narrowing down information sources in this embodiment. Here, the narrowing down of information sources has a correlation with knowledge quality. In other words, the more man-hours spent on narrowing down collected information sources, the more accurately it becomes possible to narrow down necessary information sources. In other words, it becomes possible to leave more accurate information sources. Therefore, the more man-hours spent on narrowing down information sources, the more the knowledge quality improves.

[0047] Next, FIG. 6C is a diagram showing the labor-hours-knowledge quantity / quality model 174-3 for the conversion of narrowed-down information sources into knowledge in this embodiment, that is, the creation of knowledge. Here, knowledge creation has a correlation with knowledge quantity. In other words, the more labor-hours spent on knowledge creation, the more knowledge quantity improves. However, once a certain amount of knowledge has been created, information sources will run out and the improvement in knowledge quantity will reach an upper limit. Therefore, as shown in FIG. 6C, in the labor-hours-knowledge quantity / quality model 174-3, the knowledge quantity will improve until the labor-hours reach a certain level, and thereafter the knowledge quantity will be maintained.

[0048] Next, FIG. 6D is a diagram showing the man-hours-knowledge quantity / quality model 174-4 for knowledge registration created in this embodiment. Here, knowledge registration has a correlation with knowledge quantity. In other words, the more man-hours required for knowledge registration, the more the knowledge quantity improves. However, once a certain amount of knowledge has been registered, there will be no more knowledge to register, and the improvement in knowledge quantity will reach an upper limit. Therefore, as shown in FIG. 6D, in the man-hours-knowledge quantity / quality model 174-4, the knowledge quantity will improve until the man-hours reach a certain level, and thereafter the knowledge quantity will be maintained.

[0049] Next, Fig. 6E is a diagram showing a man-hours-knowledge quantity / quality model 174-5 for knowledge updating in this embodiment. Here, knowledge updating has a correlation with knowledge quantity and knowledge quality. Note that up to knowledge registration, it indicates the man-hours required to introduce the knowledge system, and updating indicates the man-hours required after introduction.

[0050] Furthermore, (1) the more man-hours spent on updating knowledge, the more accurate knowledge remains, and the better the knowledge quality. (2) Furthermore, in updating knowledge, the quantity of new knowledge increases, and the quality of knowledge improves. Therefore, as shown in FIG. 6E, in the man-hour-knowledge quantity / quality model 174-5, the knowledge quantity improves according to the man-hours. Note that in FIG. 6E, the model may be configured so that the knowledge quantity improves until the man-hours reach a certain level and then the knowledge quantity is maintained. Also, in the man-hour-knowledge quantity / quality model 174-5, the more man-hours are increased, the better the knowledge quality becomes, and once a certain level of knowledge quality is reached, the quality is maintained thereafter. Note that the creation of the man-hour-knowledge quantity / quality models 174-1 to 174-5 shown in FIGS. 6A to 6E will be described later using FIG. 9.

[0051] Next, FIG. 7 is a diagram showing a knowledge quantity / quality-acquisition performance model 175 in this embodiment. FIG. 7 shows a representative example of the knowledge quantity / quality-acquisition performance model 175, and specific examples will be described later. FIG. 7 shows the relationship between knowledge quantity and / or quality and knowledge acquisition performance. For knowledge acquisition performance, accuracy, granularity, and speed are used. This knowledge quantity / quality-acquisition performance model 175 is created based on the labor-hours-knowledge quantity / quality model 174, but it is desirable to further apply a quantity / quality relationship definition model 176. The quantity / quality relationship definition model 176 will be described below.

[0052] 8A and 8B are diagrams showing the quantity-quality relationship definition model 176 in this embodiment. The quantity-quality relationship definition model 176 is information that classifies knowledge quality and knowledge quality performance and shows them in a matrix. Depending on the knowledge quantity and knowledge quality, the performance is classified into insufficient knowledge quantity, excessive knowledge quality, optimal, reduced acquisition performance, and insufficient knowledge quality. This classification can be determined based on the knowledge quantity definition table 172 and the knowledge quality definition table 173. For this reason, knowledge quantity and knowledge quality such as "medium" and "excessive" may be provided in the knowledge quantity definition table 172 and the knowledge quality definition table 173 of this embodiment. The classification may also be determined by specification by the user.

[0053] Then, depending on which of these categories the knowledge to be analyzed falls into, a knowledge quantity / quality-acquisition performance model 175 can be calculated. This calculation will be explained later using FIG. 9, but the knowledge quantity / quality-acquisition performance model 175 is calculated according to the dashed lines (indicating fixed knowledge quality and knowledge quantity) shown in FIGS. 8A and 8B. Of these, FIG. 8A shows a situation where knowledge quality ranges from excessive to low. Also, FIG. 8B shows a situation where knowledge quantity ranges from excessive to low.

[0054] Here, we will explain each classification. First, insufficient knowledge quantity means that the knowledge quality is appropriate or excessive, but the knowledge quantity is insufficient. Furthermore, excessive knowledge quality means that the knowledge quality is excessive compared to the required specifications, or that the knowledge quality is sufficient but the labor hours (cost) are higher than the budget or other standards. For example, this refers to a state in which further improvement in knowledge quality will not contribute to improving knowledge acquisition performance, such as accuracy.

[0055] Furthermore, "decreased acquisition performance" refers to a state in which knowledge quality is sufficient but the amount of knowledge is large, resulting in a decrease in knowledge acquisition performance, particularly speed. As a result, although accuracy is high, it takes time to acquire knowledge. Furthermore, "insufficient knowledge quality" refers to a state in which knowledge quality is insufficient, regardless of the amount of knowledge. Note that in Figures 8A and 8B, the area below "insufficient knowledge amount" is referred to as "insufficient knowledge quality," but this may also be referred to as "insufficient knowledge quantity." In this way, the quantity-quality relationship definition model 176 is not limited to Figures 8A and 8B.

[0056] Also, cost-effectiveness 177 is information that includes man-hours and knowledge acquisition performance, which are profit / loss indicators, and indicates the relationship between them. Specific details will be explained later when explaining the display screen of FIG.

[0057] This concludes the explanation of the information in this embodiment, and next we will explain the processing flow in this embodiment. Figure 9 is a flowchart showing the processing flow in this embodiment. Each step in Figure 9 will be explained below, and the processing will mainly use the configuration in Figure 1.

[0058] First, in step S1, the man-hour acquisition unit 12 acquires the man-hours required to introduce the knowledge system to be analyzed. At this time, the man-hour acquisition unit 12 may acquire related information such as the required specifications, budget, and personnel for the knowledge system. Then, modeling is performed in two stages: man-hour-knowledge quantity / quality modeling processing and knowledge quantity / quality-acquired performance modeling processing. The details are explained below.

[0059] First, the man-hour-knowledge quantity / quality modeling process will be described. In this process, the man-hour-knowledge quantity / quality modeling unit 13 models the relationship between the acquired man-hours and the knowledge quantity indicating the amount of knowledge in the target knowledge system and the knowledge quality indicating the quality of the knowledge, to calculate the man-hour-knowledge quantity / quality model 174. That is, steps S2 to S6 are executed. In this embodiment, the man-hour-knowledge quantity / quality model 174 is calculated by dividing the intervals and linearly approximating them, taking into account the man-hour thresholds until the improvement of the knowledge quantity and knowledge quality reaches an upper limit, that is, until saturation.

[0060] In step S2, the man-hour-knowledge quantity / quality modeling unit 13 creates a model of the collection of information sources and the knowledge quantity. That is, the man-hour-knowledge quantity / quality modeling unit 13 calculates a man-hour-knowledge quantity / quality model 174-1 for the collection of information sources. To this end, the man-hour-knowledge quantity / quality modeling unit 13 uses the following (Equation 1) and (Equation 2). p=ax(x≦T)...(Number 1) p = aT(x>T) (Equation 2) Here, x: man-hours, p: quantity of knowledge, a: proportionality coefficient (manually set as appropriate), and T: man-hour threshold (manually set as appropriate). As mentioned above, the man-hours-knowledge quantity / quality model 174-1 is a model related to knowledge quantity, and therefore does not take knowledge quality into consideration. However, it is also possible to perform calculations in which knowledge quality is set to a constant value such as q=0. This also applies to steps S3 and onwards.

[0061] Furthermore, in step S3, the man-hour-knowledge quantity / quality modeling unit 13 creates a model of the narrowing down of information sources and knowledge quality. That is, the man-hour-knowledge quantity / quality modeling unit 13 calculates a man-hour-knowledge quantity / quality model 174-2 for the narrowing down of information sources. To this end, the man-hour-knowledge quantity / quality modeling unit 13 uses the following (Equation 3) and (Equation 4). q=bx(x≦T) (Equation 3) q=bT(x>T) (Equation 4) Here, x: man-hours, q: quantity of knowledge, b: proportionality coefficient (manually set as appropriate), T: man-hour threshold (manually set as appropriate). As mentioned above, the man-hours-knowledge quantity / quality model 174-2 is a model related to knowledge quality, and therefore does not take into account the quantity of knowledge.

[0062] Furthermore, in step S4, the man-hour-knowledge quantity / quality modeling unit 13 creates a model of knowledge creation of the information source and knowledge quantity. That is, the man-hour-knowledge quantity / quality modeling unit 13 calculates a man-hour-knowledge quantity / quality model 174-3 for knowledge creation of the information source (knowledge creation). To do this, the man-hour-knowledge quantity / quality modeling unit 13 uses (Equation 1) and (Equation 2) in the same way as in step S2.

[0063] Furthermore, in step S5, the man-hour-knowledge quantity / quality modeling unit 13 creates a model of knowledge registration and knowledge quantity. That is, the man-hour-knowledge quantity / quality modeling unit 13 calculates a man-hour-knowledge quantity / quality model 174-4 for knowledge registration. To this end, the man-hour-knowledge quantity / quality modeling unit 13 uses (Equation 1) and (Equation 2) in the same way as in step S2.

[0064] Furthermore, in step S6, the man-hour-knowledge quantity / quality modeling unit 13 creates a model of knowledge update and knowledge quantity / quality. That is, the man-hour-knowledge quantity / quality modeling unit 13 calculates a man-hour-knowledge quantity / quality model 174-5 for knowledge update. To this end, the man-hour-knowledge quantity / quality modeling unit 13 uses (Equation 1) to (Equation 4). Through the above man-hour-knowledge quantity / quality modeling process, the man-hour-knowledge quantity / quality models 174-1 to 174-5 shown in FIGS. 6A to 6E are created. This concludes the description of the man-hour-knowledge quantity / quality modeling process. However, this modeling process is based on past estimation results, and estimation results including cost and knowledge acquisition performance may be reused. Estimation data showing the estimation results includes required specifications in addition to man-hours and amounts, so man-hours and amounts can be used as costs, and required specifications can be used as knowledge acquisition performance.

[0065] Next, the knowledge quantity / quality-acquisition performance modeling process will be described. In the knowledge quantity / quality-acquisition performance modeling process, the knowledge quantity / quality-acquisition performance modeling unit 14 calculates the knowledge quantity / quality-acquisition performance model 175 using the man-hour-knowledge quantity / quality models 174-1 to 174-5 and the quantity / quality relationship definition model 176. Here, in this embodiment, any one of the knowledge quantity, knowledge quality, and knowledge acquisition performance is fixed or specified to calculate the knowledge quantity / quality-acquisition performance model 175. As a result, it is possible to create knowledge quantity / quality-acquisition performance models 175 for each of the accuracy / granularity and speed of knowledge acquisition performance. Note that in this embodiment, accuracy / granularity are lumped together, but these may also be calculated as separate models.

[0066] First, in step S7, the knowledge quantity / quality - acquisition performance modeling unit 14 selects whether to fix or specify any one of the knowledge quantity, knowledge quality, and knowledge acquisition performance. For this purpose, the knowledge quantity / quality - acquisition performance modeling unit 14 may receive the selection from the input unit 11, or may sequentially select the knowledge quantity, knowledge quality, and knowledge acquisition performance. As a result of this selection, when fixing the knowledge quality (quality fixing), the process proceeds to step S8. Also, when fixing the knowledge quantity (quantity fixing), the process proceeds to step S10. Further, when specifying the knowledge acquisition performance, the process proceeds to step S12. Hereinafter, each process will be described.

[0067] In step S8, the knowledge quantity / quality - acquisition performance modeling unit 14 reads out the quantity / quality relationship definition model 176 and fixes the knowledge quality. For this purpose, the knowledge quantity / quality - acquisition performance modeling unit 14 fixes, that is, sets, each of the knowledge qualities: low to excessive shown by the dashed - dot line in FIG. 8A.

[0068] Also, in step S9, the knowledge quantity / quality - acquisition performance modeling unit 14 calculates the knowledge quantity / quality - acquisition performance model 175 for each set knowledge quality. Specifically, the knowledge quantity / quality - acquisition performance models 175 - 1 to 175 - 4 shown in FIGS. 10A to 10D are calculated. Here, the knowledge quantity / quality - acquisition performance models 175 - 1 to 175 - 4 respectively show the relationship between the accuracy and granularity (accuracy / granularity), which are the knowledge acquisition performances, and the knowledge quantity. For this purpose, the knowledge quantity / quality - acquisition performance modeling unit 14 uses the following (Equation 5) to (Equation 9). b = aq (q ≤ T3) ··· (Equation 5) b = aT3 (q > T3) ··· (Equation 6) y = bp (p ≤ T1) ··· (Equation 7) y = bT1+bc(p - T1) (T1 < p ≤ T2) ··· (Equation 8) y = bT{1}+bc(T2 - T1) (T2 < p) ··· (Equation 9) where y: precision / granularity, p: knowledge quantity, q: knowledge quality, a, b: proportionality coefficients, c: attenuation factor, T1, T2: thresholds for knowledge quantity, T3: thresholds for knowledge quality. Note that y may be an index that combines precision and granularity, or it may indicate either one of them.

[0069] Next, specific details of the knowledge quantity / quality-acquisition performance models 175-1 to 175-4 shown in FIGS. 10A to 10D will be described. FIG. 10A is a diagram showing the knowledge quantity / quality-acquisition performance model 175-1 in this embodiment. In FIG. 10A, the knowledge quantity / quality-acquisition performance modeling unit 14 fixes the knowledge quality in the quantity / quality relationship definition model 176 to "low." In FIG. 10A, the accuracy / granularity improves as the knowledge quantity increases up to T1 (see equation 7). Furthermore, after T1, the accuracy / granularity saturates and there is almost no improvement. Here, the knowledge quantity / quality-acquisition performance modeling unit 14 determines that the knowledge quality is insufficient overall using the quantity / quality relationship definition model 176.

[0070] FIG. 10B is a diagram showing a knowledge quantity / quality-acquisition performance model 175-2 in this embodiment. In FIG. 10B, the knowledge quantity / quality-acquisition performance modeling unit 14 fixes the knowledge quality in the quantity / quality relationship definition model 176 to "medium." In FIG. 10B, the accuracy / granularity improves as the knowledge quantity increases up to T1 (see Equation 7). Here, the knowledge quantity / quality-acquisition performance modeling unit 14 determines, using the quantity / quality relationship definition model 176, that the knowledge quantity is insufficient. From T1 to T2, the accuracy / granularity improves as the knowledge quantity increases, but the degree of improvement is lower than up to T1 (see Equation 8). Here, the knowledge quantity / quality-acquisition performance modeling unit 14 determines, using the quantity / quality relationship definition model 176, that the accuracy / granularity is optimal. From T2 onward, the accuracy / granularity saturates, and there is almost no improvement (see Equation 9). Here, the knowledge quantity / quality-acquisition performance modeling unit 14 uses the quantity / quality relationship definition model 176 to determine that the acquisition performance has deteriorated.

[0071] FIG. 10C is a diagram showing a knowledge quantity / quality-acquisition performance model 175-3 in this embodiment. In FIG. 10C, the knowledge quantity / quality-acquisition performance modeling unit 14 fixes the knowledge quality in the quantity / quality relationship definition model 176 to "high." In FIG. 10C, the accuracy / granularity improves as the knowledge quantity increases up to T1 (see Equation 7). Here, the knowledge quantity / quality-acquisition performance modeling unit 14 determines, using the quantity / quality relationship definition model 176, that the knowledge quantity is insufficient. From T1 to T2, the accuracy / granularity improves as the knowledge quantity increases, but the degree of improvement is lower than up to T1 (see Equation 8). Here, the knowledge quantity / quality-acquisition performance modeling unit 14 determines, using the quantity / quality relationship definition model 176, that the accuracy / granularity is optimal. From T2 onward, the accuracy / granularity saturates, and there is almost no improvement (see Equation 9). Here, the knowledge quantity / quality-acquisition performance modeling unit 14 uses the quantity / quality relationship definition model 176 to determine that the acquisition performance has deteriorated.

[0072] FIG. 10D is a diagram showing a knowledge quantity / quality-acquisition performance model 175-4 in this embodiment. In FIG. 10D, the knowledge quantity / quality-acquisition performance modeling unit 14 fixes the knowledge quality in the quantity / quality relationship definition model 176 to "excessive." In FIG. 10D, the accuracy / granularity improves as the knowledge quantity increases up to T1 (see Equation 7). Here, the knowledge quantity / quality-acquisition performance modeling unit 14 determines, using the quantity / quality relationship definition model 176, that the knowledge quantity is insufficient. Furthermore, from T1 to T2, the accuracy / granularity improves as the knowledge quantity increases, but the degree of improvement is lower than up to T1 (see Equation 8). Here, the knowledge quantity / quality-acquisition performance modeling unit 14 determines, using the quantity / quality relationship definition model 176, that the knowledge quantity is excessive. From T2 onwards, the accuracy / granularity saturates, and there is almost no improvement (see Equation 9). Here, the knowledge quantity / quality-acquisition performance modeling unit 14 uses the quantity / quality relationship definition model 176 to determine that the acquisition performance has deteriorated.

[0073] Above, the description of step S9 is completed. Subsequently, step S10 will be described. In step S10, the knowledge quantity / quality - acquisition performance modeling unit 14 reads the quantity / quality relationship definition model 176 and fixes the knowledge quantity. For this purpose, the knowledge quantity / quality - acquisition performance modeling unit 14 fixes, that is, sets, each of the knowledge quantities: less to excessive shown by the dashed - dotted line in FIG. 8B.

[0074] Also, in step S11, the knowledge quantity / quality - acquisition performance modeling unit 14 calculates the knowledge quantity / quality - acquisition performance model 175 for each set knowledge quantity. Specifically, the knowledge quantity / quality - acquisition performance models 175 - 5 to 175 - 8 shown in FIGS. 11A to 11D are calculated. Here, the knowledge quantity / quality - acquisition performance models 175 - 5 to 175 - 8 also show the relationship between the accuracy and granularity, which are the knowledge acquisition performances, and the knowledge quantity. For this purpose, the knowledge quantity / quality - acquisition performance modeling unit 14 uses the following (Equation 10) to (Equation 14). <​​​​​​​​​​​​​​Next, specific details of the knowledge quantity / quality-acquisition performance models 175-5 to 175-8 shown in FIGS. 11A to 11D will be described. FIG. 11A is a diagram showing the knowledge quantity / quality-acquisition performance model 175-5 in this embodiment. In FIG. 11A, the knowledge quantity / quality-acquisition performance modeling unit 14 fixes the knowledge quantity in the quantity / quality relationship definition model 176 to "low." In FIG. 11A, the accuracy / granularity improves as the knowledge quality increases up to T4 (see equation 12). Furthermore, after T4, the accuracy / granularity saturates and there is almost no improvement. Here, the knowledge quantity / quality-acquisition performance modeling unit 14 determines that the knowledge quality is insufficient overall using the quantity / quality relationship definition model 176.

[0076] 11B is a diagram showing a knowledge quantity / quality-acquisition performance model 175-6 in this embodiment. In FIG. 11B, the knowledge quantity / quality-acquisition performance modeling unit 14 fixes the knowledge quantity in the quantity / quality relationship definition model 176 to "medium." In FIG. 11B, the precision / granularity improves as the knowledge quality increases up to T1 (see equation 12). Here, the knowledge quantity / quality-acquisition performance modeling unit 14 determines that the knowledge quality is insufficient using the quantity / quality relationship definition model 176.

[0077] Furthermore, from T4 to T5, the accuracy / granularity improves as the knowledge quality increases, but the degree of improvement is lower than up to T5 (see equation 13). Here, the knowledge quantity / quality-acquisition performance modeling unit 14 determines that it is optimal using the quantity / quality relationship definition model 176. Then, from T5 onwards, the accuracy / granularity saturates and there is almost no improvement (see equation 14). Here, the knowledge quantity / quality-acquisition performance modeling unit 14 determines that the knowledge quality is excessive using the quantity / quality relationship definition model 176.

[0078] FIG. 11C is a diagram showing a knowledge quantity / quality-acquisition performance model 175-7 in this embodiment. In FIG. 11C, the knowledge quantity / quality-acquisition performance modeling unit 14 fixes the knowledge quantity in the quantity / quality relationship definition model 176 to "large." In FIG. 11C, the accuracy / granularity improves as the knowledge quantity increases up to T4 (see equation 12). Here, the knowledge quantity / quality-acquisition performance modeling unit 14 determines that the knowledge quality is insufficient using the quantity / quality relationship definition model 176. After T4, the accuracy / granularity improves as the knowledge quantity increases, but the degree of improvement decreases compared to up to T4 (see equation 13). Here, the knowledge quantity / quality-acquisition performance modeling unit 14 determines that the acquisition performance has deteriorated using the quantity / quality relationship definition model 176.

[0079] 11D is a diagram showing a knowledge quantity / quality-acquisition performance model 175-8 in this embodiment. In FIG. 11D, the knowledge quantity / quality-acquisition performance modeling unit 14 fixes the knowledge quantity in the quantity / quality relationship definition model 176 to "excessive." In FIG. 11D, the precision / granularity improves as the knowledge quantity increases up to T4 (see equation 12). Here, the knowledge quantity / quality-acquisition performance modeling unit 14 determines that the knowledge quality is insufficient using the quantity / quality relationship definition model 176.

[0080] Furthermore, from T4 onwards, the precision / granularity improves in accordance with an increase in the knowledge quantity, but the degree of improvement is lower than up to T4 (see equation 13). Here, the knowledge quantity / quality-acquisition performance modelling unit 14 determines that the acquisition performance has deteriorated using the quantity / quality relationship definition model 176.

[0081] This concludes the explanation of step S11, and next we will explain step S12. In step S12, the knowledge quantity / quality-acquisition performance modeling unit 14 identifies the knowledge acquisition performance to be modeled. Here, since accuracy and granularity are handled as the knowledge acquisition performance in steps S8 to S11, speed is identified.

[0082] Also, in step S13, similar to steps S8 to S11, a knowledge amount / quality-acquisition performance model 175 for each knowledge quality and a knowledge amount / quality-acquisition performance model 175 for each knowledge amount are calculated. At this time, the knowledge amount / quality-acquisition performance modeling unit 14 determines whether the speed is fast or slow using the speed threshold value K.

[0083] First, the calculation of the knowledge amount / quality-acquisition performance model 175 for each knowledge quality will be described. Similar to step S8, the knowledge amount / quality-acquisition performance modeling unit 14 reads the amount / quality relationship definition model 176 and fixes the knowledge quality.

[0084] Also, the knowledge amount / quality-acquisition performance modeling unit 14 calculates the knowledge amount / quality-acquisition performance model 175 for each set knowledge quality, similar to step S9. Specifically, the knowledge amount / quality-acquisition performance models 175-9 to 175-12 shown in FIGS. 12A to 12D are calculated. Here, the knowledge amount / quality-acquisition performance models 175-9 to 175-12 respectively show the relationship between the speed, which is the knowledge acquisition performance, and the knowledge amount. For this purpose, the knowledge amount / quality-acquisition performance modeling unit 14 uses the following (Equation 15) to (Equation 19). b = aq (q ≤ T3) ··· (Equation 15) b = aT3 (q > T3) ··· (Equation 16) z = bp (p ≤ T1) ··· (Equation 17) z = bT1 + bc(p - T1) (T1 < p ≤ T2) ··· (Equation 18) z = bT1 + bc(T2 - T1) (T2 < p) ··· (Equation 19) Here, z: speed, p: knowledge amount, q: knowledge quality, a, b: proportionality coefficients, c: attenuation factor, T1, T2: knowledge amount thresholds, T3: knowledge quality threshold.

[0085] Next, specific details of the knowledge quantity / quality-acquisition performance models 175-9 to 175-12 shown in FIGS. 12A to 12D will be described. FIG. 12A is a diagram showing the knowledge quantity / quality-acquisition performance model 175-9 in this embodiment. In FIG. 12A, the knowledge quantity / quality-acquisition performance modeling unit 14 fixes the knowledge quality in the quantity / quality relationship definition model 176 to "low." In FIG. 12A, the speed improves as the knowledge quantity increases up to T1 (see equation 17). Furthermore, the speed decreases after T1. Here, the knowledge quantity / quality-acquisition performance modeling unit 14 determines that the knowledge quality is insufficient overall using the quantity / quality relationship definition model 176. Furthermore, the knowledge quantity / quality-acquisition performance modeling unit 14 determines that the speed is slow regardless of the knowledge quantity using a threshold value K.

[0086] FIG. 12B is a diagram showing a knowledge quantity / quality-acquisition performance model 175-10 in this embodiment. In FIG. 12B, the knowledge quantity / quality-acquisition performance modeling unit 14 fixes the knowledge quality in the quantity / quality relationship definition model 176 to "medium." In FIG. 12B, the speed improves as the knowledge quantity increases up to T1 (see equation 17). Here, the knowledge quantity / quality-acquisition performance modeling unit 14 determines that the knowledge quantity is insufficient using the quantity / quality relationship definition model 176. Furthermore, from T1 to T2, the speed improves as the knowledge quantity increases, but the degree of improvement is lower than up to T1 (see equation 18). Here, the knowledge quantity / quality-acquisition performance modeling unit 14 determines that the speed is optimal using the quantity / quality relationship definition model 176.

[0087] Then, from T2 onwards, the speed decreases (see equation 19). Here, the knowledge quantity / quality-acquisition performance modeling unit 14 determines that the acquisition performance has decreased using the quantity / quality relationship definition model 176. Furthermore, the knowledge quantity / quality-acquisition performance modeling unit 14 determines, using the threshold value K, that the speed is generally slow up to T1, the speed is fast from T1 to T2, and the speed is fast from T2 onwards but slows down as the knowledge quantity increases.

[0088] 12C is a diagram showing a knowledge quantity / quality-acquisition performance model 175-11 in this embodiment. In FIG. 12C, the knowledge quantity / quality-acquisition performance modeling unit 14 fixes the knowledge quality in the quantity / quality relationship definition model 176 to "high." In FIG. 12C, the speed improves as the knowledge quantity increases up to T1 (see Equation 17).

[0089] Here, the knowledge quantity / quality-acquisition performance modeling unit 14 determines that the knowledge quantity is insufficient using the quantity / quality relationship definition model 176. Also, from T1 to T2, the speed improves as the knowledge quantity increases, but the degree of improvement is lower than up to T1 (see equation 18). Here, the knowledge quantity / quality-acquisition performance modeling unit 14 determines that the knowledge quantity is optimal using the quantity / quality relationship definition model 176.

[0090] Then, from T2 onwards, the speed decreases (see equation 19). Here, the knowledge quantity / quality-acquisition performance modeling unit 14 determines that the acquisition performance has decreased using the quantity / quality relationship definition model 176. Furthermore, the knowledge quantity / quality-acquisition performance modeling unit 14 determines, using the threshold value K, that the speed is generally slow up to T1, the speed is fast from T1 to T2, and the speed is fast from T2 onwards but slows down as the knowledge quantity increases.

[0091] 12D is a diagram showing a knowledge quantity / quality-acquisition performance model 175-12 in this embodiment. In FIG. 12D, the knowledge quantity / quality-acquisition performance modeling unit 14 fixes the knowledge quality in the quantity / quality relationship definition model 176 to "excessive." In FIG. 12D, the speed improves as the knowledge quantity increases up to T1 (see equation 17). Here, the knowledge quantity / quality-acquisition performance modeling unit 14 determines that the knowledge quantity is insufficient using the quantity / quality relationship definition model 176.

[0092] Also, between T1 and T2, as the amount of knowledge increases, the speed improves, but the degree of improvement is lower than that until T1 (see Fig. 18). Here, the knowledge amount / quality - acquisition performance modeling unit 14 will determine it as excessive using the amount / quality relationship definition model 176. And after T2, the speed will decrease (see Fig. 19). Here, the knowledge amount / quality - acquisition performance modeling unit 14 will determine it as a decrease in acquisition performance using the amount / quality relationship definition model 176. Furthermore, the knowledge amount / quality - acquisition performance modeling unit 14 determines that up to T1, the speed is generally slow, between T1 and T2 the speed is fast, and after T2 the speed is fast but the speed will slow down as the amount of knowledge increases, based on the threshold value K.

[0093] The above concludes the description of the calculation of the knowledge amount / quality - acquisition performance model 175 for each knowledge quality. Subsequently, the calculation of the knowledge amount / quality - acquisition performance model 175 for each knowledge amount will be described.

[0094] Similar to step S10, the knowledge amount / quality - acquisition performance modeling unit 14 reads out the amount / quality relationship definition model 176 and fixes the amount of knowledge. Also, similar to step S11, the knowledge amount / quality - acquisition performance modeling unit 14 calculates the knowledge amount / quality - acquisition performance model 175 for each set amount of knowledge. Specifically, the knowledge amount / quality - acquisition performance models 175 - 13 to 175 - 16 shown in Figs. 13A to 13D are calculated. Here, the knowledge amount / quality - acquisition performance models 175 - 13 to 175 - 16 also each show the relationship between the speed, which is the specified acquisition performance, and the amount of knowledge. For this purpose, the knowledge amount / quality - acquisition performance modeling unit 14 uses the following (Equation 20) to (Equation 24). b = ap (p ≤ T6) ··· (Equation 20) b = aT6 (p > T6) ··· (Equation 21) z = bq (q ≤ T4) ··· (Equation 22) z = bT4 + bc(q - T4) (T4 < q ≤ T5) ··· (Equation 23) z = bT4 + bc(T5 - T4) (T5 < q) ··· (Equation 24) Here, z is the speed, p is the amount of knowledge, q is the quality of knowledge, a and b are proportional coefficients, c is the attenuation factor, T4 and T5 are the thresholds of the quality of knowledge, and T6 is the threshold of the amount of knowledge.

[0095] Next, specific details of the knowledge quantity / quality-acquisition performance models 175-13 to 175-16 shown in FIGS. 13A to 13D will be described. FIG. 13A is a diagram showing the knowledge quantity / quality-acquisition performance model 175-13 in this embodiment. In FIG. 13A, the knowledge quantity / quality-acquisition performance modeling unit 14 fixes the knowledge quantity in the quantity / quality relationship definition model 176 to "small." Furthermore, in FIG. 13A, the speed improves as the knowledge quality increases up to T4 (see equation 22). Furthermore, after T4, the speed saturates and there is almost no improvement. Here, the knowledge quantity / quality-acquisition performance modeling unit 14 determines that the knowledge quality is insufficient overall using the quantity / quality relationship definition model 176. Furthermore, the knowledge quantity / quality-acquisition performance modeling unit 14 determines that the speed is slow regardless of the knowledge quantity using a threshold value K.

[0096] FIG. 13B is a diagram showing a knowledge quantity / quality-acquisition performance model 175-14 in this embodiment. In FIG. 13B, the knowledge quantity / quality-acquisition performance modeling unit 14 fixes the knowledge quantity in the quantity / quality relationship definition model 176 to "medium." In FIG. 13B, the speed improves as the knowledge quality increases up to T1 (see Equation 22). Here, the knowledge quantity / quality-acquisition performance modeling unit 14 determines that the knowledge quality is insufficient using the quantity / quality relationship definition model 176. Furthermore, from T4 to T5, the speed improves as the knowledge quality increases, but the degree of improvement is lower than up to T5 (see Equation 23). Here, the knowledge quantity / quality-acquisition performance modeling unit 14 determines that the speed is optimal using the quantity / quality relationship definition model 176. After T5, the speed decreases (see Equation 24). Here, the knowledge quantity / quality-acquisition performance modeling unit 14 determines that there is an excess of knowledge quality using the quantity / quality relationship definition model 176. Furthermore, the knowledge quantity / quality-acquisition performance modeling unit 14 determines, using the threshold value K, that the speed is generally slow up to T1, the speed is fast from T1 to T2, and the speed is fast from T2 onwards but slows down as the knowledge quantity increases.

[0097] FIG. 13C is a diagram showing a knowledge quantity / quality-acquisition performance model 175-15 in this embodiment. In FIG. 13C, the knowledge quantity / quality-acquisition performance modeling unit 14 fixes the knowledge quantity in the quantity / quality relationship definition model 176 to "large." In FIG. 13C, the speed improves as the knowledge quantity increases up to T4 (see equation 22). After T4, the speed saturates and there is almost no improvement. Here, the knowledge quantity / quality-acquisition performance modeling unit 14 determines that the acquisition performance has deteriorated overall using the quantity / quality relationship definition model 176. Furthermore, the knowledge quantity / quality-acquisition performance modeling unit 14 determines that the speed is slow regardless of the knowledge quantity using the threshold value K.

[0098] FIG. 13D is a diagram showing a knowledge quantity / quality-acquisition performance model 175-16 in this embodiment. In FIG. 13D, the knowledge quantity / quality-acquisition performance modeling unit 14 fixes the knowledge quantity in the quantity / quality relationship definition model 176 to "excessive." In FIG. 13D, the speed improves as the knowledge quantity increases up to T4 (see equation 22). After T4, the speed saturates and there is almost no improvement. Here, the knowledge quantity / quality-acquisition performance modeling unit 14 determines that the acquisition performance has deteriorated overall using the quantity / quality relationship definition model 176. Furthermore, the knowledge quantity / quality-acquisition performance modeling unit 14 determines that the speed is slow regardless of the knowledge quantity based on the threshold value K. This concludes the description of the knowledge quantity / quality-acquisition performance modeling process up to step S13. In this embodiment, the labor-hours-knowledge quantity / quality model 174 and the knowledge quantity / quality / acquisition performance model 175 are created through two-stage modeling: labor-hours-knowledge quantity / quality modeling processing and knowledge quantity / quality / acquisition performance modeling processing. However, the labor-hours-knowledge quantity / quality modeling unit 13 or the knowledge quantity / quality / acquisition performance modeling unit 14 may combine these two calculated models into one common model. This common model indicates the relationship between labor hours and knowledge acquisition performance, that is, the relationship between cost and information acquisition performance.

[0099] Furthermore, in step S14, the cost-effectiveness calculation unit 15 calculates the cost-effectiveness 177 of introducing the knowledge system using the labor-knowledge quantity / quality model 174 and the knowledge quantity / quality / acquisition performance model 175. Preferably, the cost-effectiveness calculation unit 15 stores the calculated cost-effectiveness 177 in the storage unit 17. Here, the cost-effectiveness 177 includes labor and knowledge acquisition performance, which are gain / loss indicators. Then, in step S15, the output unit 16 outputs the cost-effectiveness created by the cost-effectiveness calculation unit 15. For example, the processing device 101 in FIG. 2 causes the communication device 102 to transmit the cost-effectiveness 177 to the terminal devices 2-1, 2-2, and the terminal device 3. The terminal devices 2-1, 2-2, and the terminal device 3 then display the transmitted cost-effectiveness 177.

[0100] Here, the cost-effectiveness 177 will be explained using its display screen. FIG. 14 is a diagram showing display screens 160-1 to 160-3 of the cost-effectiveness 177 in this embodiment. Display screen 160-1 shows the created cost-effectiveness 177. Display screen 106-1 displays accuracy (%), granularity (items), and speed as knowledge acquisition performance. Then, each man-hour corresponding to this knowledge acquisition performance is displayed. That is, information source collection: 20, information source narrowing: 10, information source knowledge conversion: 5, and knowledge registration: 5 are each displayed in person-days. Furthermore, knowledge update, which is one of the man-hours, is displayed as 5 (per month) because it is the man-hour after implementation. Note that although FIG. 14 is shown as display screen 160, it also represents cost-effectiveness 177.

[0101] As described above, cost-effectiveness 177 includes knowledge acquisition performance and man-hours, which are examples of profit-loss indicators. According to a more preferred aspect of this embodiment, the profit-loss indicators in cost-effectiveness 177 have a corresponding relationship with each other. For example, by changing the knowledge acquisition performance through user operation, the man-hours are also changed accordingly. This corresponding relationship may be included in cost-effectiveness 177, or may be determined by the man-hours-knowledge quantity / quality model 174 and the knowledge quantity / quality-acquisition performance model 175. Furthermore, the common model described above may also be determined. Such interactive changes will be described below.

[0102] In FIG. 14, it is assumed that the accuracy, granularity, and speed of knowledge acquisition performance 160-21 have been changed from display screen 160-1 to display screen 160-2. That is, the input unit 11 receives a change instruction from the user to improve accuracy and granularity, such as accuracy: 90, granularity: 5, and speed: slow. Then, the cost-effectiveness calculation unit 15 calculates a labor-hour value indicating the labor hours corresponding to the change to knowledge acquisition performance, based on the correspondence between labor hours and knowledge acquisition performance. As a result, the labor hours 160-22, namely, information source collection: 40, information source narrowing: 20, and information source knowledgeization: 10, are changed (increased). Therefore, the user can understand that the labor hours, that is, costs, will increase.

[0103] It is desirable that the changed knowledge acquisition performance 160-21 and man-hours 160-22 be displayed in a format that is distinct from other items, as shown in the figure. For example, this can be achieved by changing the font (type, color (e.g., red text), size, contrast, etc.), background color, or design. Alternatively, other parts may be displayed in a more subdued manner.

[0104] In this way, display screen 160-2 shows that the man-hours have been changed in accordance with the change in knowledge acquisition performance. However, conversely, knowledge acquisition performance may be changed in accordance with the change in man-hours. This example will be explained using display screen 160-3 in FIG. 14. In display screen 160-3, compared to display screen 160-1, information source narrowing 160-32 has been changed from 10 to 5. In other words, input unit 11 receives a change instruction from the user to change information source narrowing: 5.

[0105] Then, the cost-effectiveness calculation unit 15 calculates and changes the knowledge acquisition performance according to the change in the narrowing down of information sources based on the correspondence between the man-hours and the knowledge acquisition performance. As a result, the accuracy of the knowledge acquisition performance is changed to 70, and the granularity is changed to 7. Therefore, the user can understand that the knowledge acquisition performance, accuracy, and granularity will decrease.

[0106] This concludes the description of this embodiment, but the present invention is not limited to this. For example, knowledge systems other than those for investigating the causes of failures are also applicable. Such knowledge systems include those that perform sales know-how management and customer management. Information search systems other than knowledge systems may also be applicable. Furthermore, the present invention may be realized by a computer device such as a PC other than the cloud system shown in FIG. 2. Furthermore, the cost-effectiveness calculation device 1 may be realized as a function of an estimation device or estimation system. [Explanation of symbols]

[0107] 1...cost-effectiveness calculation device, 11...input unit, 12...man-hour acquisition unit, 13...man-hour-knowledge quantity / quality modeling unit, 14...knowledge quantity / quality-acquisition performance modeling unit, 15...cost-effectiveness calculation unit, 16...output unit, 17...storage unit, 101...processing device, 102...communication device, 103...main memory device, 104...sub-memory device, 105...cost-effectiveness calculation program, 106...man-hour acquisition module, 107...man-hour-knowledge quantity / quality modeling module, 1 08...Knowledge quantity / quality-acquisition performance modeling module, 109...Cost-effectiveness calculation module, 171...Knowledge, 172...Knowledge quantity definition table, 173...Knowledge quality definition table, 174...Man-hours-knowledge quantity / quality model, 175...Knowledge quantity / quality-acquisition performance model, 176...Quantity / quality relationship definition model, 177...Cost-effectiveness, 2-1, 2-2...Terminal device, 20...Enterprise system, 3...Terminal device, 4...Database system

Claims

1. A cost-effectiveness calculation method executed by a cost-effectiveness calculation device that calculates the cost-effectiveness of introducing a system having an information acquisition function that acquires information for solving a problem, a cost acquisition unit that acquires the cost of introducing the system; a first modeling unit calculating a first model indicating a relationship between the cost, an information amount indicating an amount of the information, and an information quality indicating a quality of the information; a second modeling unit, using the first model, calculating a second model indicating a relationship between the information amount and the information quality and information acquisition performance indicating performance of an information acquisition function; a cost-effectiveness calculation unit uses the first model and the second model to calculate cost-effectiveness in introducing the system, including the cost and the information acquisition performance, which are profit / loss indicators in introducing the system; A cost-effectiveness calculation method in which an output unit outputs the cost-effectiveness.

2. The cost-effectiveness calculation method according to claim 1, the cost-effectiveness calculation device further includes an input unit that receives a change instruction for changing either the cost or the information acquisition performance of the cost-effectiveness output by a user as a first profit / loss index; The cost-effectiveness calculation unit changes the profit / loss index in response to the change instruction, and changes a second profit / loss index in response to the change in the first profit / loss index; The cost-effectiveness calculation method, wherein the output unit outputs cost-effectiveness including the changed first profit-loss index and second profit-loss index.

3. The cost-effectiveness calculation method according to claim 2, The system is a knowledge system that processes knowledge as the information, the first modeling unit uses a knowledge quantity indicating the amount of the knowledge as the information quantity and a knowledge quality indicating the quality of the knowledge as the information quality; A cost-effectiveness calculation method in which the second modeling unit calculates the second model using the accuracy, granularity, and speed of the knowledge system, which are the information acquisition performance.

4. The cost-effectiveness calculation method according to claim 3, The knowledge is a collection of data items that are connected to each other according to relationships; the amount of knowledge indicates the quantity of the data items; A cost-effectiveness calculation method in which the knowledge quality indicates the degree of at least one of accuracy, completeness, appropriateness, availability, and usability of knowledge.

5. The cost-effectiveness calculation method according to claim 4, The cost is the number of steps required to implement the knowledge system, The man-hours include: a collection of sources of potential data items of said knowledge; Narrowing down the sources of information collected; Converting the narrowed down information sources into knowledge, Registration of the created knowledge; Update of registered knowledge A cost-effectiveness calculation method that is at least one of the labor hours.

6. The cost-effectiveness calculation method according to claim 5, The man-hours are man-hours for collecting information sources, narrowing down the information sources, converting the information sources into knowledge, registering the knowledge, and updating the knowledge, The first modeling unit generates the first model as follows: a man-hours-knowledge quantity / quality model showing the relationship between the man-hours for collecting the information source, turning the information source into knowledge, and updating the knowledge and the knowledge quantity; A man-hours-knowledge quantity / quality model showing the relationship between the man-hours for narrowing down the information sources and the knowledge quality; A cost-effectiveness calculation method for calculating a man-hours-knowledge quantity / quality model showing the relationship between the man-hours required to register the knowledge and the knowledge quantity and knowledge quality.

7. The cost-effectiveness calculation method according to claim 6, The second modeling unit generates, as the second model, A knowledge quantity / quality-acquisition performance model showing the relationship between the precision and granularity and the knowledge quantity when the knowledge quality is fixed; a knowledge quantity / quality-acquisition performance model showing the relationship between the accuracy and granularity and the knowledge quality when the knowledge quantity is fixed; a knowledge quantity / quality-acquisition performance model showing the relationship between the speed and the knowledge quantity; A cost-effectiveness calculation method for calculating a knowledge quantity / quality-acquisition performance model showing the relationship between the speed and the knowledge quality.

8. The cost-effectiveness calculation method according to claim 1, The cost-effectiveness calculation method, wherein the second modeling unit aggregates the first model and the second model to calculate a common model that indicates the relationship between the cost and the information acquisition performance.

9. A cost-effectiveness calculation device for calculating the cost-effectiveness of introducing a system having an information acquisition function for acquiring information for solving a problem, a cost acquisition unit that acquires the cost of introducing the system; a first modeling unit that calculates a first model that indicates a relationship between the cost, an information amount that indicates the amount of the information, and an information quality that indicates the quality of the information; a second modeling unit that uses the first model to calculate a second model that indicates a relationship between the information amount and the information quality and an information acquisition performance that indicates a performance of an information acquisition function; a cost-effectiveness calculation unit that calculates cost-effectiveness in introducing the system, including the cost and the information acquisition performance, which are profit / loss indicators in introducing the system, using the first model and the second model; A cost-effectiveness calculation device having an output unit that outputs the cost-effectiveness.

10. 10. The cost-effectiveness calculation device according to claim 9, further comprising an input unit for receiving a change instruction for either the cost or the information acquisition performance of the cost-effectiveness output by a user as a first profit / loss index; The cost-effectiveness calculation unit changes the profit / loss index in response to the change instruction, and changes a second profit / loss index in response to the change in the first profit / loss index; The output unit outputs the cost-effectiveness including the changed first profit-loss index and the changed second profit-loss index.

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